• Aucun résultat trouvé

Cet ouvrage présente les travaux de recherches reliés au transfert de connaissances de la prédiction de performance de matériel graphique, du domaine de développement embarqué ou pour ordinateur de bureau, vers le domaine de l’avionique. Une nouvelle méthode est présentée permettant d’effectuer de la prédiction de performance pour du matériel graphique avionique. Aucune autre méthode n’existe dans la littérature pour ce problème au niveau du domaine de l’avionique. Toutefois, les résultats obtenus sont similaires à ceux d’autres méthodes existantes pour le domaine d’ordinateur de bureau ou embarqué. Les travaux futurs porteront sur l’ajout de nouvelles fonctionnalités présentes dans la prochaine version d’OpenGL SC, tels les effets de brouillard ou l’anticrénelage. De plus, il est intéressant d’orienter l’utilisation de cette méthode de prédiction de performance vers un simulateur de matériel graphique avionique. Cet outil ajouterait une latence artificielle qui permettrait à des développeurs d’exécuter leur logiciel graphique sur leur station de travail possédant du matériel conventionnel, tout en obtenant un avant-goût des performances réelles qu’atteindrait leur logiciel sur le matériel avionique du système final. Il serait aussi intéressant de généraliser davantage les modèles de performance afin d’émettre des prédictions à partir d’un seul modèle contenant toutes les caractéristiques de scènes. Cela permettrait de réduire la taille prise en mémoire en remplaçant l’arbre de modèles par un unique modèle ainsi que de réduire le temps d’entraînement.

RÉFÉRENCES

[1] V. a. B. Hilderman, Len, Avionics Certification: A Complete Guide to DO-178 (Software),

DO-254 (Hardware), 1st ed. USA: Avionics Communications Inc., 2007.

[2] M. a. K. Dutton, D, "The challenges of graphics processing in the avionics industry," in

Digital Avionics Systems Conference (DASC), 2010 IEEE/AIAA 29th, 2010, pp. 5.A.1-1-

5.A.1-9.

[3] R. Moller, "State-of-the-Art 3D Graphics for Embedded Systems," in Devices, Circuits and

Systems, Proceedings of the 6th International Caribbean Conference on, ed, 2006, pp. 339-

343.

[4] Z. a. Y. Wang, Hui and Zhou, Xiuzhi, "A Simulation Method of Reconfigurable Airborne Display and Control System," in Proceedings of the First Symposium on Aviation

Maintenance and Management-Volume II. vol. 297, J. Wang, Ed., ed: Springer Berlin

Heidelberg, 2014, pp. 255-263.

[5] (9 Janvier). ARINC-661 Widget Creation, Presagis Inc. Available:

http://www.presagis.com/solutions/arinc_661_widget_creation/

[6] V. Legault, "Méthodologie expérimentale pour évaluer les caractéristiques des plateformes graphiques avioniques," M. Sc. A., Département de génie informatique et logiciel, École Polytechnique de Montréal, Montréal, Canada, 2014.

[7] (2009). OpenGL SC Safety Critical Profile. Available: https://www.khronos.org/openglsc/

[8] P. Cole, "OpenGL ES SC - open standard embedded graphics API for safety critical applications," in Digital Avionics Systems Conference, 2005. DASC 2005. The 24th, 2005, p. 8.

[9] Rightware. (Janvier 2015). Basemark ES 3.0. Available:

http://www.rightware.com/benchmarks/basemark-es-3-0/

[10] S. P. E. Corporation. (2007, Janvier 2015). What is This Thing Called "SPECviewperf®"? Available: https://www.spec.org/gwpg/gpc.static/whatis_vp8.html

[11] S. K. a. D. Manocha, "Hierarchical back-face culling," ed. Chapel Hill, NC, USA: Tech. Rep, 1996.

[12] H. Z. a. K. E. H., "Fast backface culling using normal masks," ed: SI3D, 1997, pp. 103- 106.

[13] J. Gregory, "The Rendering Pipeline," in Game Engine Architecture, C. Press, Ed., 2nd Edition ed, 2014.

[14] D. M. H. Zhang, T. Hudson, and K.E. Hoff, "Visibility Culling Using Hierarchical Occlusion Maps " in Proc. ACM SIGGRAPH ’97 1997, pp. 77-88.

[15] V. H. J. Bittner, and P. Slavik, "Hierarchical Visibility Culling with Occlusion Trees " in

Proc. Computer Graphics Int’l, 1998, pp. 207-219.

[16] I. P. a. S. Tzafestas, "Occlusion culling algorithms: A comprehensive survey " J. Intell.

[17] H.-Y. K. a. C.-H. Y. a. L.-S. Kim, "A Memory-Efficient Unified Early Z-Test,"

Visualization and Computer Graphics, IEEE Transactions on, vol. 17, pp. 1286-1294,

2011.

[18] D. K. C.-H. Yu, and L.-S. Ki, "An Area Efficient Early Z-Test Method for 3D Graphics Rendering Hardwar " IEEE Trans.Circuits and Systems I vol. 55, pp. 1929-1938, 2008. [19] C.-L. W. Y.M. Tsao, S.-Y. Chien, and L.-G. Chen, "Adaptive Tile Depth Filter for the

Depth Buffer Bandwidth Minimization in the Low Power Graphics Systems " in Proc.

IEEE Int’l Symp. Circuits and Systems 2006, pp. 5023-5026.

[20] H. Hoppe, "Optimization of Mesh Locality for Transparent Vertex Caching," in

Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, New York, NY, USA, 1999, pp. 269-276.

[21] D. N. a. J. B. a. P. V. Sander, "Triangle Order Optimization for Graphics Hardware Computation Culling " in Symposium on Interactive 3D Graphics and Games, 2006. [22] G. a. Y. Lin, T.P.-Y., "An improved vertex caching scheme for 3D mesh rendering,"

Visualization and Computer Graphics, IEEE Transactions on, vol. 12, pp. 640-648, 2006.

[23] S.-E. a. L. Yoon, Peter and Pascucci, Valerio and Manocha, Dinesh, "Cache-oblivious Mesh Layouts," ACM Trans. Graph., vol. 24, pp. 886-893, 2005.

[24] P. V. a. N. Sander, Diego and Barczak, Joshua, "Fast Triangle Reordering for Vertex Locality and Reduced Overdraw," ACM Trans. Graph., vol. 26, 2007.

[25] Nvidia. (2015, 3 Mars). Nvidia Nsight. Available:

http://www.nvidia.com/object/nsight.html

[26] AMD. (2015, 3 Mars). GPU PerfStudio. Available: http://developer.amd.com/tools-and- sdks/graphics-development/gpu-perfstudio/

[27] D. Kanter. (2011, 19 Janvier). "Predicting AMD and Nvidia GPU Performance", Real

World Technologies. Available: http://www.realworldtech.com/amd-nvidia-gpu- performance/

[28] Z. Yao and J. D. Owens, "A quantitative performance analysis model for GPU architectures," in High Performance Computer Architecture (HPCA), 2011 IEEE 17th

International Symposium on, 2011, pp. 382-393.

[29] S. Hong and H. Kim, "An Analytical Model for a GPU Architecture with Memory-level and Thread-level Parallelism Awareness," SIGARCH Comput. Archit. New, vol. 37, pp. 152-163, 2009.

[30] L. Weiguo, W. Muller-Wittig, and B. Schmidt, "Performance Predictions for General- Purpose Computation on GPUs," in ICPP 2007. International Conference on, 2007, p. 50. [31] S. S. Baghsorkhi, M. Delahaye, S. J. Patel, W. D. Gropp, and W.-m. W. Hwu, "An Adaptive Performance Modeling Tool for GPU Architectures," SIGPLAN Not., vol. 45, pp. 105-114, 2010.

[32] J. Stratton, S. Stone, and W.-m. Hwu, "MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs," in Languages and Compilers for Parallel Computing. vol. 5335, ed: Springer Berlin Heidelberg, 2008.

[33] S. Collange, M. Daumas, D. Defour, and D. Parello, "Barra: A Parallel Functional Simulator for GPGPU," in Modeling, Analysis Simulation of Computer and

Telecommunication Systems (MASCOTS), 2010 IEEE International Symposium on, 2010,

pp. 351-360.

[34] A. Bakhoda, G. L. Yuan, W. W. L. Fung, H. Wong, and T. M. Aamodt, "Analyzing CUDA workloads using a detailed GPU simulator," in Performance Analysis of Systems and

Software, 2009. ISPASS 2009. IEEE International Symposium on, Boston, MA, USA, 2009,

pp. 163-174.

[35] A. Kerr, G. Diamos, and S. Yalamanchili, "Modeling GPU-CPU Workloads and Systems," in Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics

Processing Units, Pittsburgh, Pennsylvania, USA, 2010, pp. 31-42.

[36] L. Eeckhout, R. Sundareswara, J. J. Yi, D. J. Lilja, and P. Schrater, "Accurate statistical approaches for generating representative workload compositions," in Workload

Characterization Symposium, 2005. Proceedings of the IEEE International, 2005, pp. 56-

66.

[37] S. Che and K. Skadron, "BenchFriend: Correlating the performance of GPU benchmarks,"

International Journal of High Performance Computing Applications, vol. 28, pp. 238-250,

2014.

[38] D. J. Kerbyson, H. J. Alme, A. Hoisie, F. Petrini, H. J. Wasserman, and M. Gittings, "Predictive Performance and Scalability Modeling of a Large-scale Application," in

Proceedings of the 2001 ACM/IEEE Conference on Supercomputing, Denver, Colorado,

USA, 2001, p. 37.

[39] A. Snavely, L. Carrington, N. Wolter, J. Labarta, R. Badia, and A. Purkayastha, "A Framework for Performance Modeling and Prediction," in Supercomputing, ACM/IEEE

2002 Conference, 2002, p. 21.

[40] L. T. Yang, X. Ma, and F. Mueller, "Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution," in Supercomputing, 2005. Proceedings of the

ACM/IEEE SC 2005 Conference, 2005, p. 40.

[41] B. C. Lee and D. M. Brooks, "Accurate and Efficient Regression Modeling for Microarchitectural Performance and Power Prediction," SIGPLAN Not., vol. 41, pp. 185- 194, 2006.

[42] B. C. Lee and D. M. Brooks, "Illustrative Design Space Studies with Microarchitectural Regression Models," in High Performance Computer Architecture, 2007. HPCA 2007.

IEEE 13th International Symposium on, 2007, pp. 340-351.

[43] P. J. Joseph, K. Vaswani, and M. J. Thazhuthaveetil, "Construction and use of linear regression models for processor performance analysis " in International Symposium on

High-Performance Computer Architecture - HPCA, 2006, pp. 99-108.

[44] G. Marin and J. Mellor-Crummey, "Cross-architecture Performance Predictions for Scientific Applications Using Parameterized Models," SIGMETRICS Perform. Eval. Rev., vol. 32, pp. 2-13, 2004.

[45] M. Faerman, A. Su, R. Wolski, and F. Berman, "Adaptive Performance Prediction for Distributed Data-intensive Applications," in Proceedings of the 1999 ACM/IEEE

Conference on Supercomputing, Portland, Oregon, USA, 1999.

[46] Collectif. (2015). Machine Learning. Available:

http://en.wikipedia.org/wiki/Machine_learning

[47] Collectif. (2015). Decision Tree Learning. Available:

http://en.wikipedia.org/wiki/Decision_tree_learning

[48] Collectif. (2015). Random Forest. Available: http://en.wikipedia.org/wiki/Random_forest

[49] Collectif. (2015). Gradient Boosting. Available:

http://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting

[50] B. Li, L. Peng, and B. Ramadass, "Accurate and efficient processor performance prediction via regression tree based modeling," Journal of Systems Architecture, vol. 55, pp. 457-467, 2009.

[51] E. Ould-Ahmed-Vall, J. Woodlee, C. Yount, K. A. Doshi, and S. Abraham, "Using Model Trees for Computer Architecture Performance Analysis of Software Applications," in

Performance Analysis of Systems Software, 2007. ISPASS 2007. IEEE International Symposium on, 2007, pp. 116-125.

[52] Y. Zhang, Y. H. Bin Li, and L. Pen, "Performance and Power Analysis of ATI GPU: A Statistical Approach," in Networking, Architecture and Storage (NAS), 2011 6th IEEE

International Conference on, 2011, pp. 149-158.

[53] Mathworks. (2015). Improve Neural Network Generalization and Avoid Overfitting. Available: http://www.mathworks.com/help/nnet/ug/improve-neural-network- generalization-and-avoid-overfitting.html

[54] Collectif. (2015). Artificial neural network. Available:

http://en.wikipedia.org/wiki/Artificial_neural_network

[55] E. Ipek, B. de Supinski, M. Schulz, and S. McKee, "An Approach to Performance Prediction for Parallel Applications," in Euro-Par 2005 Parallel Processing. vol. 3648, ed: Springer Berlin Heidelberg, 2005, pp. 196-205.

[56] B. C. Lee, D. M. Brooks, B. R. de Supinski, M. Schulz, K. Singh, and S. A. McKee, "Methods of Inference and Learning for Performance Modeling of Parallel Applications," in Proceedings of the 12th ACM SIGPLAN Symposium on Principles and Practice of

Parallel Programming, San Jose, California, USA, 2007, pp. 249-258.

[57] R. M. Yoo, H. Lee, K. Chow, and H.-H. S. Lee, "Constructing a Non-Linear Model with Neural Networks for Workload Characterization," in Workload Characterization, 2006

IEEE International Symposium on, 2006, pp. 150-159.

[58] P. J. Joseph, K. Vaswani, and M. J. Thazhuthaveetil, "A Predictive Performance Model for Superscalar Processors," in Microarchitecture, 2006. MICRO-39. 39th Annual IEEE/ACM

International Symposium on, 2006, pp. 161-170.

[59] E. Ïpek, S. A. McKee, R. Caruana, B. R. de Supinski, and M. Schulz, "Efficiently Exploring Architectural Design Spaces via Predictive Modeling," SIGARCH Comput. Archit. News, vol. 34, pp. 195-206, 2006.

[60] S. J. Tarsa, A. P. Kumar, and H. T. Kung, "Workload Prediction for Adaptive Power Scaling Using Deep Learning," in IC Design & Technology (ICICDT), 2014 IEEE

International Conference on, 2014, pp. 1-5.

[61] V. Zaccaria, G. Palermo, F. Castro, C. Silvano, and G. Mariani, "Multicube Explorer: An Open Source Framework for Design Space Exploration of Chip Multi-Processors," presented at the Architecture of Computing Systems (ARCS), 2010 23rd International

Conference on, 2010.

[62] T. Chai and R. R. Draxler, "Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature.," Geoscientific Model

Development, vol. 7, pp. 1247-1250, 2014.

[63] M. Boyer, J. Meng, and K. Kumaran, "Improving GPU Performance Prediction with Data Transfer Modeling," in Parallel and Distributed Processing Symposium Workshops PhD

Forum (IPDPSW), 2013 IEEE 27th International, 2013, pp. 1097-1106.

[64] N. Baek and G. J. Baeck, "Design of OpenGL SC emulation library over the desktop OpenGL 1.3," in Digital Avionics Systems Conference (DASC), 2010 IEEE/AIAA 29th, 2010, pp. 6.D.2-1-6.D.2-8.

[65] (2015). PowerVR SDK. Available:

http://community.imgtec.com/developers/powervr/graphics-sdk/

Documents relatifs